Background

Founding developers

BBA & NJT

Long-term collaborators

\(+\) neurodebian, slicer, brainsfit, nipype, itk and more …

a pride: common way of doing things

… in a competitive world …

Definitions

  • Registration \(=\) estimate an “optimal” geometric mapping between image pairs or image sets (e.g. Affine)

  • Similarity \(=\) a function relating one image to another, given a transformation (e.g. mutual information)

  • Diffeomorphisms \(=\) differentiable map with differentiable inverse (e.g. “silly putty”, viscous fluid)

  • Segmentation \(=\) labeling tissue or anatomy in images, usually automated (e.g. K-means)

  • Multivariate \(=\) using many voxels or measurements at once (e.g. PCA, \(p >> n\) ridge regression)

  • Multiple modality \(=\) using many modalities at once (e.g. DTI and T1 and BOLD)

  • MALF: multi-atlas label fusion - using anatomical dictionaries to label new data

  • Solutions to challenging statistical image processing problems usually need elements from each of the above

Image mapping & perception: 1878

  • Francis Galton: Can we see criminality in the face?

  • (maybe he should have used ANTs?)

Image mapping & biology: 1917

D’Arcy Thompson

Initial scope

… just do a better registration (tell story) …

ANTs Lineage

References: @Horn1981, @Gee1993, @Grenander1993, @Thompson2001, @Miller2002, @Shen2002, @Arnold2014, @Thirion1998, @Rueckert1999, @Fischl2012, @Ashburner2012

Diffeomorphisms

plausible physical modeling of large, invertible deformations

“differentiable map with differentiable inverse”

Fine-grained and flexible maps

… to correct a misconception about diffeomorphisms …

Diffeomorphisms: image parameterization in a metric space

General purpose library for multivariate image registration, segmentation & statistical analysis tools

  • 170,000+ lines of C++, 6\(+\) years of work, 15+ collaborators.

  • Generic mathematical methods that are tunable for application specific domains: no-free lunch

  • Deep testing on multiple platforms … osx, linux, windows.

  • Several “wins” in public knock-abouts ( Klein 2009, Murphy 2011, SATA 2012 and 2013, BRATS 2013, others )

    An algorithm must use prior knowledge about a problem
    to do well on that problem

ANTs: Beyond Registration

Atropos segmentation, N4 inhomogeneity correction, Eigenanatomy, SCCAN, Prior-constrained PCA, and atlas-based label fusion and MALF (powerful expert systems for segmentation)

On documentation

documentation is important

On documentation

… developers can be blind to doc deficiencies

while users are blind to what we provide!

Competitions

Competitions

Klein 2009

  • 14 algorithms
  • publicly available data
  • different labeling protocols
  • developers tune their own algorithms

Results

“One of the most significant findings of this study is that the relative performances of the registration methods under comparison appear to be little affected by the choice of subject population, labeling protocol, and type of overlap measure…. ART, SyN, IRTK, and SPM’s DARTEL Toolbox gave the best results according to overlap and distance measures, with ART and SyN delivering the most consistently high accuracy across subjects and label sets.”

EMPIRE 2010

  • Alignment of lung boundaries,
  • major fissures,
  • annotated landmark pairs, and
  • topology of displacement field.

Results

Multi-Atlas 2012

Results

SATA 2013

BRATS 2013

Results

Optimal symmetric multimodal templates and concatenated random forests for supervised brain tumor segmentation (simplified) with ANTsR, Neuroinformatics.

STACOM 2014

ANTs and the perils of circularity

“Logical circularity in voxel-based analysis: normalization strategy may induce statistical bias”

ANTs and the perils of circularity

Command line help

$ CreateDTICohort -h

COMMAND:
     CreateDTICohort

OPTIONS:
     -d, --image-dimensionality 2/3
     -a, --dti-atlas inputDTIAtlasFileName
     -x, --label-mask-image maskImageFileName
                            lowerThresholdFunction
     -n, --noise-sigma <noiseSigma=18>
     -p, --pathology label[<percentageChangeEig1=-0.05>,<percentageChangeAvgEig2andEig3=0.05>,<numberOfVoxels=all or percentageOfvoxels>]
     -w, --dwi-parameters [B0Image,directionFile,bvalue]
                          [B0Image,schemeFile]
     -r, --registered-population textFileWithFileNames.txt
     -o, --output [outputDirectory,fileNameSeriesRootName,<numberOfControls=10>,<numberOfExperimentals=10>]
     -h
     --help

Which one is not warped?

Answer